Client
A Milan-based startup that creates AI-powered cashierless checkout technology for physical stores
Industry
Retail
Services
Product Development, UX/UI, AI/ML, QA
Tech
TypeScript, ReactJS, Node.js, MongoDB

Challenge

Artificial intelligence is reshaping the shopping experience in brick-and-mortar stores, enabling customers to pick up the items they want and walk out the door without having to scan codes or waiting in line to pay. More and more retailers are embracing autonomous shopping technology as AI-powered systems have made it easier to adopt. Our team helped the client harness machine learning for developing a solution that allows retail chains to make their stores checkout-free without the need to redesign them. The solution makes it possible to track shoppers’ movement and detect the items they take from the shelves using ceiling-mounted cameras.

We took on the following challenges:
Apply our skills and expertise to contribute to a rather complicated and innovative project already at a late implementation phase.
Deliver effective visualization for data used by the client’s annotation software to train its machine learning model to recognize shoppers’ actions and track their movement.
Facilitate the management of videos and sensor-provided raw data.
Implement manual testing on three target browsers.

Solution

A web app frontend built in the space of less than four months that serves as an efficient annotation tool facilitating training ML models to monitor the customer movement and shopping in a multi-camera store setting, and can run on browsers such as Google Chrome, Mozilla Firefox, and Safari.
A backend solution created using Node.js that fetches data from the client’s server.
A video and text file storage system relying on MongoDB.
A 3D reconstruction and skeleton visualization capability for action recognition.
A keypoint creation/edit feature that is vital to delivering error-free pose estimation, tracking and action recognition.
A multi-view function providing better occlusion handling and thereby boosting the ML model’s ability to correctly track customers’ movement.
A simple yet functional UI built with ReactJS that speeds up annotation and improves its efficiency.
A rigorous UI/UX testing process resulting in a few enhancements that improved the app’s performance.

Impact

Our solutions helped the client take its machine learning model training to the next level and make big strides in the fast-growing autonomous shopping technology market.
The client’s technologies attracted investor interest, resulting in its acquisition by San Francisco-based autonomous retail company.

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